Transcript GRAVEL PONDS * OF “PARQUE DEL SURESTE”

OF “PARQUE DEL SURESTE”
STUDY BY REMOTE SENSING
*
GRAVEL PONDS
Domínguez, Gómez, J.A. 2, Rejas, J.G. 1, De Miguel, E. 1, Gómez J.A. 1,
2
1
2
Ruiz-Verdú, A. ,Fernández-Renau, A. , Peña, R.
1Lab.
Central de Teledetección, INTA. Cta. Ajalvir s/n - Torrejón de Ardoz - 28850 Madrid. Spain.
2 Centro de Estudios Hidrográficos del CEDEX. Paseo Bajo de la Virgen del Puerto, 3 - 28005 Madrid. Spain.
Aims: Identification, location and thematic cartography of
water quality in the waterbodies of the “Parque Regional del
Sureste” (Madrid-Spain) by airborne multispectral images
Methodology
Planning of the field measurements
concurrent with airborne images (May
the26th and 27th, 2001)
Field measurements and image
acquisition: previous treatments.
Field research:
- Trespassing licence to
take water sampling and
field measurements in
the gravel pits.
- Gravel ponds to be
measured: 11
- Sampling points: 33
Data acquisition:
- Choice and configuration of sensors:
-Digital Camera (AMDC).
-Scanner:
-DAEDALUS 1268 (ATM).
-ETM
- Planning of the plane route and flights.
- Flying licence.
Correction of reflectivity anisotrophic effect in ATM images. Because of
Data acquisition and ATM Image correction:
this effect an attenuation in the energy upwelling of the farthest pixels
- Radiometric Homogenization.
from nadir is perceived. An empiric check is made by:
- Geometric Correction.
- Studying the mean digital level for each column in the image for the
- Mosaic.
data obtained in the research as a whole (45.000 image lines). The
statistical analysis shows that the mean energy upwelling is a parabola
Geometric correction image -image using with a maximum value at the nadir and diminishing energy between 10
and 20% in the limits of the image.
cubic convolution algorithm
(ETM-mosaic ATM)
- Studying the digital level of the same
area in two flights.
When the radiometric correction has
Bands
Bands
Wave lenght
been done the geometric distortions are
ETM
ATM
(nm)
minimized and the images are
1
2
450 - 520
georreferenced in order to locate, to
measure and to make an inventory the
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3
520 - 600
waterbodies in the “Parque del
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5
630 - 690
Sureste”.
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7
760 - 900
5
9
1550 - 1750
Image geometric correction has been done from the data collected by an
6
11
8500 - 1300
autonomous GPS placed in the plane and synchronized with the data
acquisition. To achieve an accuracy less than the pixel size (3m),
necessary in our study, a geometric correction image-map using the “thin
plate spline” algorithm has been done (digital cartography 1:5.000 of the
“Comunidad de Madrid”.
- Trophic state of the
sampling points:
55% Oligotrophic
15% Mesotrophic
9% Eutrophic
21% Hypertrophic
LIMNOLOGY USING REMOTE SENSING
1.- Determination of water bodies analysing the histogram of the near infrared band (ATM 7).
2.- Calculation of the surface and perimeter from the image. Once these data have been obtained, the shore index is
determined for each waterbody.
3.- Water quality: Locating sampling points with GPS allows their subsequent location in the image. These diglital levels
are related to the field measurements ussing multiple lineal regressions to obtain equations of surface-chlorophyll a
concentration, suspended solid concentration and Secchi disk transparency. The equation of temperature is determined
from the calibration of the sensor termic band.
[Clfa] (mg/m3) = 15,625e(-0,503ATM2 +0,56·ATM3-1,617)
RMS = 2,3 mg/m3
[SS] (mg/l) = 0,001444·(ATM7)2+0,0314·(ATM)2-11,219
RMS = 1,4 mg/l
TS (m) = 0,82·(ATM2)-0,434·ATM3) – 1,617
RMS = 0,2 m
T (ºC) = 7,44 +0,38·(ATM11)
RMS = 0,2 ºC
4.- Results: 103 technical filing cards of all waterbodies and analysis of the limnologycal information.
Distribución del Número de Masas de Agua
en función de su Índice de Orilla
Estado Trófico de las Masas de Agua
120
4%
10%
114
11%
49%
26%
Número de Masas de Agua
100
80
84
70
60
40
20
Ultraoligotrófico
Oligotrófico
Mesotrófico
Eutrófico
Hipertrófico
0
1,5
2
y mayor...
Índice de Orilla
250
Distribución del Número de Masas de Agua estudiadas
en función de su Superficie
y = 26.785x-1.5395
R2 = 0.81
225
110
200
100
104
90
175
81
[Cla](mg/m3)
Nº de Masas de Agua
80
70
60
50
52
150
Correlación entre la
Transparencia y la
Clorofila
125
100
40
75
30
20
50
23
10
7
0
100
1000
10000
100000
Superficie (m2)
1000000
1
25
y mayor...
0
0
0,5
1
1,5
2
2,5
3
3,5
Transparencia (m)
4
4,5
5
5,5
6
6,5
* Gravel ponds = Gravel pit ponds = Ponds generated in gravel mining pits